Loading EpiEstimApp
To load the app, the easiest way is to open a web browser and simply navigate to the online interface. If you choose this approach, type in the web address below and skip to “Loading the data” below.
www.XXX.XXX
Alternatively, to load EpiEstimApp in R, first install devtools if you have not already done so, install EpiEstimApp, and launch EpiEstimApp.
install.packages("devtools")
devtools::install_github("jstockwin/EpiEstimApp")
EpiEstimApp::runEpiEstimApp()
If the app is not now open in your web browser, look for “Listening on http://127.0.0.1:xxxx” in your R console and open that page.
knitr::include_graphics("appFrontPage.jpg")
Loading the data and estimating the reproduction number
The example that follows will use disease incidence time series (DataS1.csv) and interval-censored observations of the serial interval (DataS2.csv), which are datasets available at XXX. Save these on your computer.
Since we will be uploading the first of these files as the incidence data, select “Own data” and click “Next”.
knitr::include_graphics("STEP2.jpg")
Click browse, and choose the incidence file XXX.csv from your computer to upload. Now select the length of the sliding time window W over which EpiEstim will estimate the time-dependent reproduction number. Here we choose W = 6. In this case the reproduction number will then be estimated over windows [1,7], [2,8],… Also choose the gamma distributed prior parameter values for the reproduction number, and then click “Next”.
knitr::include_graphics("STEP3.jpg")
Since the incidence dataset that we are considering here does not contain information about cases imported from elsewhere, click “No” and then “Next”.
knitr::include_graphics("STEP5.jpg")
We are going to upload interval censored patient data on the serial interval, so click “Individual Patient Data” and “Next”.
knitr::include_graphics("STEP6.jpg")
